Asphalt mixture mineral aggregate void rate prediction method and computer equipment

Abstract

The invention discloses an asphalt mixture mineral aggregate void rate prediction method and computer equipment, and relates to the field of asphalt pavement materials.The method comprises the steps that particle size distribution of asphalt mixtures of different proportions is obtained; respectively calculating the reference total specific volumes of the asphalt mixtures with different proportions according to the particle size distribution; calculating the comprehensive total specific volume of the asphalt mixture according to the reference total specific volume of all the asphalt mixtures; calculating the mineral aggregate void rate of the asphalt mixture according to the comprehensive total specific volume; the calculation of the reference total specific volume of the asphalt mixture comprises the following steps: calculating the main control particle size of the asphalt mixture according to the particle size distribution of the asphalt mixture; calculating a main particle size range according to the main control particle size; dividing the asphalt mixture into skeleton particles, filling particles and suspended particles according to the main particle size range; calculating to obtain an optimal distribution function by using an optimization algorithm; respectively calculating specific volumes of the skeleton particles, the filling particles and the suspended particles based on the main particle size range and the optimal distribution function; calculating the reference total specific volume of the asphalt mixture according to the specific volumes of the skeleton particles, the filling particles and the suspended particles; the method has the advantages that the design efficiency of the asphalt mixture is improved, and the research and development cost is reduced.

Description

Technical Field

[0001] This invention relates to the field of asphalt pavement materials, and in particular to a method and computer equipment for predicting the void ratio of asphalt mixture aggregates. Background Technology

[0002] Void ratio (VMA) is an important volumetric parameter in asphalt mixture design. VMA refers to the percentage of void volume between aggregate skeletons in the total mixture volume. These voids provide space for asphalt and ensure sufficient durability and stability of the mixture.

[0003] If the VMA is too low, it means that the asphalt mastic content is insufficient, which can easily lead to premature hardening of the mixture and a decrease in its resistance to fatigue cracking. If the VMA is too high, it may mean that the aggregate skeleton structure is poor and the asphalt content is too high, which will significantly weaken the rutting ability of the mixture.

[0004] Currently, the main method for determining the VMA (Volume Amount) of asphalt mixtures is through laboratory testing. This process typically involves: weighing aggregates according to the target gradation, heating them, mixing them with asphalt, molding specimens using a rotary compactor (SGC) or Marshall compactor, and finally calculating the VMA through volumetric parameter measurements.

[0005] While this method is direct, it has significant limitations: the experimental process is time-consuming, inefficient, and costly, and it cannot reveal the formation mechanism.

[0006] Therefore, there is an urgent need in this field to invent a scientific, accurate and effective method for predicting the void ratio of aggregates in asphalt mixtures, so as to fundamentally improve the design efficiency of asphalt mixtures and reduce research and development costs. Summary of the Invention

[0007] The problem to be solved by the present invention is to provide a method and computer equipment for predicting the void ratio of asphalt mixture aggregates, which can realize the accurate prediction of the void ratio of asphalt mixture aggregates.

[0008] To address the aforementioned technical problems, this invention provides a method and computer equipment for predicting the void ratio of asphalt mixture aggregates, comprising: acquiring particle size distributions of asphalt mixtures with different mix proportions; calculating the baseline total specific volume of asphalt mixtures with different mix proportions based on the particle size distributions; calculating the comprehensive total specific volume of asphalt mixtures based on the baseline total specific volume of all asphalt mixtures; and calculating the void ratio of asphalt mixture aggregates based on the comprehensive total specific volume. The calculation steps for the baseline total specific volume of asphalt mixtures include: calculating the main control particle size of the asphalt mixture based on the particle size distributions; calculating the main particle size range based on the main control particle size; dividing the asphalt mixture into skeleton particles, filler particles, and suspended particles based on the main particle size range; calculating the optimal distribution function using an optimization algorithm; calculating the specific volume of the skeleton particles, filler particles, and suspended particles based on the main particle size range and the optimal distribution function; and calculating the baseline total specific volume of the asphalt mixture based on the specific volumes of the skeleton particles, filler particles, and suspended particles.

[0009] As an improvement to the above scheme, the method for calculating the main control particle size of the asphalt mixture based on the particle size distribution of the asphalt mixture includes: determining the nominal maximum particle size of the asphalt mixture based on the particle size distribution of the asphalt mixture; calculating the initial control particle size based on the nominal maximum particle size; verifying the initial control particle size using the proportion of coarse aggregate; and if the proportion of coarse aggregate is within a preset range, then the initial control particle size is identified as the main control particle size.

[0010] As an improvement to the above scheme, the step of calculating the optimal distribution function using an optimization algorithm includes: constructing a quantitative difference objective function based on the particle size distribution and the distribution function; solving for the minimum value of the quantitative difference objective function using a numerical optimization algorithm based on the particle size distribution; taking the parameter corresponding to the minimum value of the quantitative difference objective function as the optimal parameter and substituting it into the distribution function to obtain the optimal distribution function.

[0011] As an improvement to the above scheme, the method of calculating the specific volume of the skeleton particles, filling particles and suspended particles using the packing model includes: calculating the total specific volume of the skeleton particles based on a mixed packing model; calculating the total specific volume of the filling particles based on a linear packing model; and calculating the total specific volume of the suspended particles based on a linear packing model.

[0012] As an improvement to the above scheme, the method for calculating the total specific volume of the skeleton particles includes: calculating the total specific volume of the skeleton particles according to the following formula:

[0013]

[0014] in, This represents the total initial specific volume of the particles that act as the framework; This represents the total initial packing density of the particles that act as the framework, and ; The interaction function represents the hybrid packing model; Indicates the upper limit of the main particle size; Indicates the lower limit of the main particle size; The proportion number is indicated as The total specific volume of the skeletal particles A; Denotes the optimal distribution function. Indicates skeletal particles As an improvement to the above solution, the method for calculating the total specific volume of the filler particles includes: calculating the total specific volume of the filler particles according to the following formula:

[0015] in, Indicates the particle reference specific volume; This indicates the particle size of the smallest particle in the asphalt mixture; Indicates the lower limit of the main particle size; Represents the optimal distribution function; Indicates components and Interaction function between them; The proportion number is indicated as The total specific volume of the filling particles B, Indicates filling particles The method for calculating the total specific volume of the suspended particles includes: calculating the total specific volume of large particles suspended in the skeleton according to the following formula:

[0016] in, Indicates the particle reference specific volume; This indicates the particle size of the largest particle in the asphalt mixture; Indicates the upper limit of the main particle size; Represents the optimal distribution function; Indicates components and The interaction function between them; The proportion number is indicated as The total specific volume of suspended particles C, This indicates suspended particles.

[0017] As an improvement to the above scheme, the method for calculating the reference total specific volume of the asphalt mixture based on the total specific volume of the skeleton particles, filler particles, and suspended particles includes: calculating the reference total specific volume of the asphalt mixture according to the following formula:

[0018] in, The proportion number is indicated as The total specific volume of the skeletal particles A, The proportion number is indicated as The total specific volume of the filling particles B, The proportion number is indicated as The total specific volume of suspended particles C, Indicates the upper limit of the main particle size. Indicates the lower limit of the main particle size. The proportion number is indicated as The baseline total specific volume of the asphalt mixture.

[0019] As an improvement to the above scheme, the method for calculating the comprehensive total specific volume of asphalt mixture based on the baseline total specific volume of all asphalt mixtures includes: calculating the comprehensive total specific volume of asphalt mixture according to the following formula:

[0020] in, This indicates the overall gross volume of the asphalt mixture. The proportion number is indicated as The baseline total specific volume of the asphalt mixture.

[0021] As an improvement to the above scheme, the method for calculating the aggregate void ratio of asphalt mixture based on the comprehensive total specific volume includes: According to the formula Calculate the aggregate void ratio of asphalt mixture; in, Indicates the overall total volume. This indicates the aggregate void ratio of asphalt mixtures.

[0022] Accordingly, the present invention also provides a computer device, including a storage device and a processor, wherein the storage device stores a computer program, characterized in that the processor executes the computer program to implement the method for predicting the void ratio of asphalt mixture aggregates as described above.

[0023] Implementing this invention has the following beneficial effects: This invention divides asphalt mixtures into three types of particles based on particle size and constructs corresponding specific volume calculation methods based on their characteristics, thereby predicting the mineral void ratio of asphalt mixtures. This achieves rapid and low-cost prediction of the mineral void ratio of asphalt mixtures, fundamentally improving the design efficiency of asphalt mixtures and reducing R&D costs. Attached Figure Description

[0024] Figure 1 This is a flowchart of an embodiment of the method for predicting the void ratio of asphalt mixture aggregates according to the present invention; Figure 2 This is a flowchart illustrating an embodiment of the method for calculating the baseline total specific volume of asphalt mixture in the method for predicting the void ratio of aggregates in asphalt mixtures according to the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. It is hereby declared that the directional terms such as up, down, left, right, front, back, inside, and outside used in this text are based solely on the accompanying drawings and are not intended to specifically limit the invention.

[0026] like Figure 1 As shown, Figure 1 A flowchart illustrating an embodiment of a method for predicting the void ratio of aggregates in asphalt mixtures is shown, comprising: S1. Obtain the particle size distribution of asphalt mixtures with different mix proportions; To predict the aggregate gaps in the target asphalt mixture, a particle size curve must first be constructed within the target range, and the gap ratio is predicted based on the curve. Based on this, the present invention first obtains a series of parameter values ​​by mixing the aggregates. The specific calculation of the relevant aggregate parameters and the coarse-fine gradation method are as follows: (1) Calculate the initial bulk density of the aggregate, which is divided into coarse aggregate and fine aggregate. The initial bulk density of the coarse aggregate is calculated as follows:

[0027] in: This indicates the initial bulk density of the coarse aggregate; Indicates the loose density of coarse aggregate; Indicates the volumetric density of coarse aggregate; This indicates the density of water.

[0028] The initial bulk density of the fine aggregate is calculated as follows:

[0029] in: Indicates the initial bulk density of fine aggregate; Indicates the bulk density of fine aggregates; Indicates the volumetric density of fine aggregates; This indicates the density of water.

[0030] (2) The coarse aggregate and fine aggregate are graded in different proportions to obtain asphalt mixtures with different proportions, and the particle size distribution of the asphalt mixtures with different proportions is calculated.

[0031] S2. Calculate and obtain the reference total specific volume of the asphalt mixture based on the particle size distribution; like Figure 2 As shown, the step of calculating and obtaining the reference total specific volume of the asphalt mixture based on the particle size distribution of the asphalt mixtures with different mix proportions includes: S21. Calculate the main control particle size of the asphalt mixture, i.e., the continuous particle size (CPS), based on the particle size distribution of the asphalt mixture. Specifically, the steps for calculating the main control particle size (CPS) of the asphalt mixture based on the parameter information include: (1) Determine the nominal maximum particle size of the asphalt mixture based on the particle size distribution of the asphalt mixture, that is, the sieve size that is one size smaller than the maximum sieve size; (2) Calculate the initial control particle size based on the nominal maximum particle size of the asphalt mixture; Specifically, the process of calculating the initial control particle size based on the nominal maximum particle size of the asphalt mixture relies on the following empirical calculation rules: If the nominal maximum particle size of the asphalt mixture is >9.5mm, then the initial control particle size is initially selected as 13.2mm = 13.2 / 2 = 6.6mm. If the nominal maximum particle size of the asphalt mixture is ≤9.5mm, then the initial control particle size is initially selected as 2.36mm. Furthermore, if the initial control particle size calculated according to empirical calculation rules is not a standard sieve aperture size, then a standard sieve aperture size one size coarser than the calculated value should be selected as the initial control particle size.

[0032] (3) The initial controlled particle size is verified using the proportion of coarse aggregate; Specifically, the step of verifying the proportion of coarse aggregate includes: Calculate whether the initial controlled particle size satisfies 0.4 ≤ CA ≤ 1.0. The formula for calculating the CA value is as follows:

[0033] in: This indicates the percentage of mass passing through the sieve apertures that primarily control particle size. This indicates the percentage of mass that passed through the first control sieve (PCS). This indicates the proportion of coarse aggregate.

[0034] It should be noted that when the CA ratio is approximately 0.4, it indicates that the coarse aggregate with a density greater than CPS has reached the densest "dry compaction" state, forming a solid skeleton; when the CA ratio is approximately 1.0, it indicates that the coarse aggregate skeleton begins to "interfere" and its density decreases; when the CA ratio is greater than 1.0, it indicates severe interference and a loose skeleton structure; when the CA ratio is less than 0.4, it indicates that there are too many fine particles in the coarse aggregate, and the skeleton may be damaged.

[0035] (4) If the proportion of coarse aggregate is within a preset range, then the initial controlled particle size is considered to be the main controlled particle size; Specifically, when the calculated coarse aggregate ratio is within the preset range, the initial controlled particle size is identified as the main controlled particle size; when the calculated coarse aggregate ratio is not within the preset range, it indicates that the initial controlled particle size calculated in step (2) is unsuitable and can be recalculated and selected.

[0036] S22. Calculate the main particle size range based on the main controlled particle size, wherein the main particle size range includes the lower limit of the main particle size and the upper limit of the main particle size. Specifically, the method for calculating the main particle size range based on the main controlled particle size (CPS) includes: The upper and lower limits of the main particle size are calculated using the following formulas:

[0037]

[0038] in: This indicates that the main control is the particle size; Indicates the upper limit of the main particle size; This indicates the lower limit of the main particle size.

[0039] Furthermore, when the calculated upper limit of the main particle size is greater than the maximum size of the particle sieve aperture, the upper limit of the main particle size is taken as the maximum size of the particle sieve aperture. And / or when the calculated lower limit of the main particle size is greater than the minimum size of the particle sieve aperture, the main particle size shall be the minimum size of the particle sieve aperture as the lower limit of the main particle size.

[0040] S23. The asphalt mixture is divided into skeleton particles, filler particles and suspended particles according to the main particle size range; Specifically, based on the compaction principle of asphalt mixtures, it is assumed that the asphalt mixture is composed of n spherical particles, and that there is an accumulation effect between the spherical particles during the compaction process of the asphalt mixture. Based on the main particle size range, the asphalt mixture is divided into skeleton particles, filler particles, and suspended particles. Skeleton particles are particles that act as the skeleton in the mixture, and their particle size range includes particles that exist between the lower limit and the upper limit of the main particle size. Filler particles are small particles that fill the skeleton, and their filler particles include particles that exist between the minimum particle size and the lower limit of the main particle size. Suspended particles are large particles that are suspended in the skeleton, and their suspended particles include particles that exist between the upper limit and the maximum particle size.

[0041] S24. Calculate the optimal distribution function using an optimization algorithm; The process of obtaining the optimal distribution function using optimization algorithms refers to the process of continuously adjusting the parameter values ​​in the distribution function using the gradation data of asphalt mixtures through optimization algorithms, so as to minimize the error between the distribution function and the actual points. More preferably, the distribution function selected in this embodiment is the Rosin-Rammler-Sperling-Bennett distribution function. No restriction is placed on the distribution function here; the optimal function can be selected based on the actual situation. The following will elaborate on this using the Rosin-Rammler-Sperling-Bennett distribution function as an example: Specifically, the process of calculating the optimal Rosin-Rammler-Sperling-Bennett distribution function includes: (1) Construct a quantitative difference objective function based on the particle size distribution and distribution function; That is, based on the Rosin-Rammler-Sperling-Bennett distribution function, a quantification objective function is defined; (1.1) The Rosin-Rammler-Sperling-Bennett distribution function is shown below:

[0042] in: Indicates particle size; Indicates the uniformity coefficient; Indicates characteristic particle size; This represents the probability density of particle size.

[0043] (1.2) Define the objective function for quantifying differences The objective function is used to quantify the overall difference between theoretical predictions and experimental values, including but not limited to the sum of squared residuals (SSR). That is, for all particle pore sizes, the square of the difference between the measured throughput and the theoretical throughput is calculated, and then summed. The optimization objective is to find the value that minimizes the SSR. , )combination.

[0044] (2) Based on the particle size distribution, use a numerical optimization algorithm to find the minimum value of the quantification difference objective function; The process of using a numerical optimization algorithm to find the minimum value of the quantization difference objective function is as follows: (2.1) Optimization algorithm selection The preferred optimization algorithm is the traditional least squares method or the Levenberg-Marquardt algorithm, but this is not a limitation; the choice can be made based on the actual amount of data required.

[0045] (2.2) Optimize algorithm execution Specifically, for the above ( , The value of ) is assigned an initial guess value, and the optimization algorithm generates a new set of parameter guess values ​​according to algorithm rules (such as calculating gradients or random search). , The quantitative difference objective function SSR is recalculated using the new parameters. Repeat the above steps until the objective function for quantifying differences meets the preset tolerance, which can be set based on experience.

[0046] (2.3) Optimization result verification and output Once the above optimization algorithm converges, the output satisfies the preset tolerance. , The value of ) is determined by visual analysis, plotting the experimental data points and the optimized distribution function on the same coordinate system. It is then checked whether the optimized distribution function passes through the experimental data points. If the optimized distribution function passes through the experimental data points, then the () is considered to be optimized. , The value is the optimal parameter calculated by the numerical optimization algorithm.

[0047] (3) Take the parameter corresponding to the minimum value of the quantitative difference objective function as the optimal parameter and substitute it into the distribution function to obtain the optimal distribution function.

[0048] S25. Based on the main particle size range and the optimal distribution function, the specific volume of the skeleton particles, filler particles and suspended particles is calculated using the packing model respectively. Specifically, the stacking model includes a linear stacking model and a hybrid stacking model; The process of calculating the specific volume of the three types of skeletal particles using the packing model is as follows: (1) Based on the mixed packing model, calculate the total specific volume of the skeleton particles:

[0049]

[0050]

[0051] in: This represents the total initial specific volume of the skeletal particles; Represents the total initial packing density of the skeletal particles and ; The interaction function represents the hybrid packing model; Indicates the upper limit of the main particle size; Indicates the lower limit of the main particle size; The proportion number is indicated as The total specific volume of the skeletal particles A; This indicates the initial specific volume of a particle of a single size; Represents the optimal distribution function; This refers to skeletal particles.

[0052] It should be noted that the initial specific volume of the single-size particles needs to be calculated experimentally using single-size particles. The specific experimental steps are as follows: (1.1) The skeleton particles are strictly sieved into a single particle size range through different standard sieves; (1.2) Particles of the single particle size range are injected into a standard container of known volume in a loose state (avoiding compaction), leveled, and the total mass is weighed. (1.3) Calculate the bulk density of loose particles of a single size; The formula for calculating the bulk density of loose material is shown below:

[0053] in: This indicates the loose bulk density of particles of a single size. This indicates the total mass of particles of a single size after they have been scraped flat. This indicates the loose volume of a single-size particle.

[0054] (1.4) Calculate the initial specific volume of a single-size particle.

[0055] The initial specific volume of a single-size particle is calculated as follows:

[0056] in: This indicates the loose bulk density of particles of a single size. This represents the initial specific volume of a single-size particle.

[0057] (2) Based on the linear packing model, calculate the total specific volume of the filling particles:

[0058]

[0059] in: Indicates the particle reference specific volume; This indicates the particle size of the smallest particle in the asphalt mixture; Indicates the lower limit of the main particle size; Represents the optimal distribution function; Indicates components and The interaction function between them; The proportion number is indicated as The total specific volume of the filling particles B; This indicates the filling particles.

[0060] It should be noted that the calculation of the particle reference specific volume needs to be performed through experimental measurement of the filled particles. The specific experimental steps are as follows: (2.1) Inject all the filling particles into a standard container of known volume in a loose state (avoid compaction), level it, and weigh the total mass. (2.2) Calculate the bulk density of the loose filling particles; (2.3) Calculate the initial specific volume of the filling particles.

[0061] The initial specific volume of a single-size particle is calculated as follows:

[0062] in: This indicates the loose bulk density of the filling particles; This indicates the initial specific volume of the filling particles.

[0063] (3) Calculate the total specific volume of the suspended particles based on the linear packing model:

[0064]

[0065] in: Indicates the particle reference specific volume; This indicates the particle size of the largest particle in the asphalt mixture; Indicates the upper limit of the main particle size; Represents the optimal distribution function; Indicates components and The interaction function between them; The proportion number is indicated as The total specific volume of suspended particles C; This indicates suspended particles.

[0066] It should be noted that the calculation of the particle reference specific volume needs to be based on experimental measurements of suspended particles. The specific experimental steps are as follows: (3.1) Inject all the suspended particles into a standard container of known volume in a loose state (avoid compaction), level it, and weigh the total mass. (3.2) Calculate the loose bulk density of the suspended particles; (3.3) Calculate the initial specific volume of the suspended particles.

[0067] The initial specific volume of a single-size particle is calculated as follows:

[0068] in: This indicates the loose bulk density of suspended particles; This represents the initial specific volume of the suspended particles.

[0069] S26. Calculate the reference total specific volume of the asphalt mixture based on the specific volumes of the skeleton particles, filler particles, and suspended particles; Specifically, the total specific volume of asphalt mixture is calculated according to the following formula:

[0070] in: The proportion number is indicated as The total specific volume of the skeletal particles A; The proportion number is indicated as The total specific volume of the filling particles B; The proportion number is indicated as The total specific volume of suspended particles C; Indicates the upper limit of the main particle size; Indicates the lower limit of the main particle size; The proportion number is indicated as The reference total volume of asphalt mixture.

[0071] S3. Calculate the total specific volume of the asphalt mixture based on the reference total specific volume of all the asphalt mixtures;

[0072] in: The proportion number is indicated as The reference total specific volume of asphalt mixture; Indicates the maximum value to filter; It represents the overall gross volume of asphalt mixture.

[0073] S4. Calculate the aggregate void ratio of the asphalt mixture based on the comprehensive total specific volume.

[0074] The aggregate void ratio of asphalt mixtures can be calculated using the following formula:

[0075] in: This indicates the overall gross volume of the asphalt mixture; This indicates the aggregate void ratio of asphalt mixtures.

[0076] The present invention will be further described below with reference to embodiments: The asphalt mixture parameters in this embodiment are shown in the table below:

[0077] The algorithm iteratively calculates the main control particle size CPS to be 4.1 mm, and further determines the upper and lower limits of the main skeleton particle size range of the asphalt mixture. The upper limit of the main particle size M = 4.1 / 0.22 = 18.64 mm, and the lower limit of the main particle size N = 4.1 * 0.22 = 0.902 mm. Considering that the maximum particle size in this embodiment is 13.2 mm, which is smaller than the calculated upper limit of the main particle size 18.64 mm, the particle size range of the asphalt mixture that plays the role of the main skeleton in this embodiment is [0.902, 13.2]. Furthermore, based on the asphalt mixture parameters and optimization algorithm, the parameters of the optimal Rosin-Rammler-Sperling-Bennett distribution function are calculated, where... , ; Based on the above particle size range results, the particles in the asphalt mixture are further divided into skeleton particles, filler particles, and suspended particles, and their baseline total specific volume is calculated for each. Finally, the comprehensive total specific volume of the asphalt mixture is calculated using the formula. ; Therefore, the predicted mineral void ratio of asphalt mixture is:

[0078] In addition, the present invention also provides a computer device, including a storage device and a processor, wherein the storage device stores a computer program, characterized in that the processor executes the computer program to implement the method for predicting the void ratio of asphalt mixture aggregates as described above.

[0079] In summary, the present invention provides a method for predicting the mineral void ratio of asphalt mixtures. By dividing asphalt mixtures into three types of particles according to their particle size and constructing corresponding specific volume calculation methods based on their characteristics, the mineral void ratio of asphalt mixtures can be predicted. This method achieves rapid and low-cost prediction of the mineral void ratio of asphalt mixtures, and fundamentally improves the design efficiency of asphalt mixtures and reduces R&D costs.

[0080] The above-disclosed embodiments are merely preferred embodiments of the present invention and should not be construed as limiting the scope of the present invention. Therefore, any equivalent variations made in accordance with the claims of the present invention are still within the scope of the present invention.

Claims

1. A method for predicting the void ratio of aggregates in asphalt mixtures, characterized in that, include The particle size distribution of asphalt mixtures with different mix proportions was obtained. The baseline total specific volume of asphalt mixtures with different proportions was calculated based on the particle size distribution. The overall total specific volume of the asphalt mixture is calculated based on the baseline total specific volume of all asphalt mixtures. The aggregate void ratio of the asphalt mixture is calculated based on the overall total specific volume. The calculation steps for the reference gross volume of the asphalt mixture include: The main control particle size of the asphalt mixture is calculated based on the particle size distribution of the asphalt mixture; Calculate the main particle size range based on the main controlled particle size; The asphalt mixture is classified into skeleton particles, filler particles, and suspended particles according to the main particle size range. The optimal distribution function is obtained by using an optimization algorithm; The specific volumes of the skeleton particles, filler particles, and suspended particles are calculated based on the main particle size range and the optimal distribution function, respectively. The baseline total specific volume of the asphalt mixture is calculated based on the specific volumes of the skeleton particles, filler particles, and suspended particles.

2. The method for predicting the void ratio of asphalt mixture aggregates as described in claim 1, characterized in that, The method for calculating the main control particle size of the asphalt mixture based on the particle size distribution of the asphalt mixture includes: The nominal maximum particle size of the asphalt mixture is determined based on the particle size distribution of the asphalt mixture. Calculate the initial control particle size based on the nominal maximum particle size; The initial controlled particle size was verified using the proportion of coarse aggregate; If the proportion of coarse aggregate is within a preset range, then the initial controlled particle size will be used as the primary controlled particle size.

3. The method for predicting the void ratio of asphalt mixture aggregates as described in claim 1, characterized in that, The steps for calculating the optimal distribution function using an optimization algorithm include: A quantitative difference objective function is constructed based on the particle size distribution and distribution function. Based on the particle size distribution, the minimum value of the quantification difference objective function is solved using a numerical optimization algorithm; The parameter corresponding to the minimum value of the objective function for quantifying differences is taken as the optimal parameter and substituted into the distribution function to obtain the optimal distribution function.

4. The method for predicting the void ratio of asphalt mixture aggregates as described in claim 1, characterized in that, The method for calculating the specific volume of the skeleton particles, filler particles, and suspended particles based on the main particle size range and the optimal distribution function includes: The total specific volume of the skeleton particles was calculated based on the hybrid packing model; The total specific volume of the filling particles is calculated based on the linear packing model; The total specific volume of the suspended particles is calculated based on the linear packing model.

5. A method for predicting the void ratio of asphalt mixture aggregates as described in claim 1 or 4, characterized in that, The method for calculating the total specific volume of the skeletal particles includes: The total specific volume of the skeletal particles is calculated using the following formula: in, This represents the total initial specific volume of the skeletal particles. This represents the total initial packing density of the skeletal particles. The interaction function represents the hybrid stacking model. Indicates the upper limit of the main particle size. Indicates the lower limit of the main particle size. The proportion number is indicated as The total specific volume of the skeletal particles A, Denotes the optimal distribution function. This refers to skeletal particles.

6. A method for predicting the void ratio of asphalt mixture aggregates as described in claim 1 or 4, characterized in that, The method for calculating the total specific volume of the filling particles includes: The total specific volume of the filling particles is calculated according to the formula: in, Indicates the particle reference specific volume. This indicates the smallest particle size in an asphalt mixture. Indicates the lower limit of the main particle size. Denotes the optimal distribution function. Indicates components and components The interaction function between them The proportion number is indicated as The total specific volume of the filling particles B, This indicates the filling particles.

7. A method for predicting the void ratio of asphalt mixture aggregates as described in claim 1 or 4, characterized in that, The method for calculating the total specific volume of the suspended particles includes: The total specific volume of the suspended particles is calculated using the following formula: in, Indicates the particle reference specific volume. This indicates the particle size of the largest particle in the asphalt mixture. Indicates the upper limit of the main particle size. Denotes the optimal distribution function. Indicates components and components The interaction function between them The proportion number is indicated as The total specific volume of suspended particles C, This indicates suspended particles.

8. The method for predicting the void ratio of asphalt mixture aggregates as described in claim 1, characterized in that, The method for calculating the reference total specific volume of the asphalt mixture based on the specific volumes of the skeleton particles, filler particles, and suspended particles includes: The reference gross volume of asphalt mixtures is calculated using the following formula: in, The proportion number is indicated as The total specific volume of the skeletal particles A, The proportion number is indicated as The total specific volume of the filling particles B, The proportion number is indicated as The total specific volume of suspended particles C, Indicates the upper limit of the main particle size. Indicates the lower limit of the main particle size. The proportion number is indicated as The baseline total specific volume of the asphalt mixture.

9. The method for predicting the void ratio of asphalt mixture aggregates as described in claim 1, characterized in that, The method for calculating the comprehensive total specific volume of asphalt mixtures based on the baseline total specific volume of all asphalt mixtures includes: The overall gross volume of asphalt mixtures is calculated using the following formula: in, This indicates the overall gross volume of the asphalt mixture. The proportion number is indicated as The baseline total specific volume of the asphalt mixture.

10. The method for predicting the void ratio of asphalt mixture aggregates as described in claim 1, characterized in that, The method for calculating the aggregate void ratio of asphalt mixture based on the comprehensive total specific volume includes: According to the formula Calculate the aggregate void ratio of asphalt mixture; in, Indicates the overall total volume. This indicates the aggregate void ratio of asphalt mixtures.

11. A computer device comprising a storage unit and a processor, wherein the storage unit stores a computer program, characterized in that, When the processor executes the computer program, it implements a method for predicting the void ratio of asphalt mixture aggregates according to any one of claims 1 to 10.

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